UAV Path Planning Design Based on Deep Learning.

UAVs are widely used in different fields of social life. With the increasing use of UAV, the main direction of future UAV technology development is “intellectualization”. Facing the problems of abundant information sources, large amount of information, interaction of various equipment systems and stability of communication signals, UAV can not only rely on manual operation, so it is important to strengthen the ability of process data to UAV. Nowadays deep learning is one of the hottest topics in the field of science and technology, and there are more and more researches based on the theory of deep learning, which provides a method for realizing artificial intelligence. This paper based on deep learning technology builds a neural network framework to study UAV path planning. Compared with the traditional UAV path algorithm, the neural network model is smaller and the recognition speed is faster. A detection and recognition method suitable for the network is proposed, which is applied to the design of UAV obstacle avoidance system to realize UAV’s recognition of the surrounding environment, obstacle avoidance and to ensure UAV’s flight safety.